# Computations
import pandas as pd
import numpy as np

# preprocessing
from sklearn import preprocessing
from sklearn.impute import SimpleImputer
import re

# Visualisation libraries

## Progress Bar
import progressbar

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns
sns.set_style("whitegrid")
sns.set_context("paper", rc={"font.size":12,"axes.titlesize":14,"axes.labelsize":12})

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from matplotlib.patches import Ellipse, Polygon
import matplotlib.gridspec as gridspec
import matplotlib.colors
from pylab import rcParams
plt.style.use('seaborn-whitegrid')
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (17, 6)
mpl.rcParams['axes.labelsize'] = 14
mpl.rcParams['xtick.labelsize'] = 12
mpl.rcParams['ytick.labelsize'] = 12
mpl.rcParams['text.color'] = 'k'
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

Telco Customer Churn

• Preprocessing
• Exploratory Data Analysis
• Modeling: PyTorch Multi-layer Perceptron (MLP) for Binary Classification
• Modeling: XGBoost Classifier

Telco Customer Churn

In this article, we analyze and predict customer churn for Telco Customer Churn data.

Dataset

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device * protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Columns	Description
customerID	Customer ID
gender	Whether the customer is a male or a female
SeniorCitizen	Whether the customer is a senior citizen or not (1, 0)
Partner	Whether the customer has a partner or not (Yes, No)
Dependents	Whether the customer has dependents or not (Yes, No)
tenure	Number of months the customer has stayed with the company
PhoneService	Whether the customer has a phone service or not (Yes, No)
MultipleLines	Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService	Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity	Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup	Whether the customer has an online backup or not (Yes, No, No internet service)
DeviceProtection	Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport	Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV	Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies	Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract	The contract term of the customer (Month-to-month, One year, Two years)
PaperlessBilling	Whether the customer has paperless billing or not (Yes, No)
PaymentMethod	The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges	The amount charged to the customer monthly
TotalCharges	The total amount charged to the customer
Churn	Whether the customer churned or not (Yes or No)

Path = 'telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv'
Data = pd.read_csv(Path)
Target = 'Churn'
Labels = ['No', 'Yes']
display(Data.head(10).style.hide_index().set_precision(2))

customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes
9305-CDSKC	Female	0	No	No	8	Yes	Yes	Fiber optic	No	No	Yes	No	Yes	Yes	Month-to-month	Yes	Electronic check	99.65	820.5	Yes
1452-KIOVK	Male	0	No	Yes	22	Yes	Yes	Fiber optic	No	Yes	No	No	Yes	No	Month-to-month	Yes	Credit card (automatic)	89.10	1949.4	No
6713-OKOMC	Female	0	No	No	10	No	No phone service	DSL	Yes	No	No	No	No	No	Month-to-month	No	Mailed check	29.75	301.9	No
7892-POOKP	Female	0	Yes	No	28	Yes	Yes	Fiber optic	No	No	Yes	Yes	Yes	Yes	Month-to-month	Yes	Electronic check	104.80	3046.05	Yes
6388-TABGU	Male	0	No	Yes	62	Yes	No	DSL	Yes	Yes	No	No	No	No	One year	No	Bank transfer (automatic)	56.15	3487.95	No

def text_sep(txt): return re.sub(r"(\w)([A-Z])", r"\1 \2", txt)
Data.rename(columns = {'gender':'Gender', 'tenure':'Tenure', 'customer ID': 'Customer ID'}, inplace = True)
Data.columns = [text_sep(txt) for txt in Data.columns.tolist()]

def Data_Plot(Inp, Title = None, W = None):
    data_info = Inp.dtypes.astype(str).to_frame(name='Data Type')
    Temp = Inp.isnull().sum().to_frame(name = 'Number of NaN Values')
    data_info = data_info.join(Temp, how='outer')
    data_info ['Size'] = Inp.shape[0]
    data_info['Percentage'] = 100 - np.round(100*(data_info['Number of NaN Values']/Inp.shape[0]),2)
    data_info = data_info.reset_index(drop = False).rename(columns = {'index':'Features'})
    #
    fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type',
                 text = 'Percentage',
                 color_discrete_sequence = ['PaleGreen', 'LightCyan', 'PeachPuff', 'Pink', 'Plum'],
                 hover_data = data_info.columns)
    fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1.01, y=.5, traceorder="normal",
                                                         bordercolor="DarkGray", borderwidth=1))
    if not W == None:
        fig.update_layout(width = W)
    fig.update_traces(texttemplate= 10*' ' + '%%{text}', textposition='inside')
    fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
    if not Title == None:
        fig.update_layout(title={'text': '<b>' + Title + '<b>', 'x':0.5,
                                         'y':0.90, 'xanchor': 'center', 'yanchor': 'top'})
    fig.show()
    return data_info
    
def dtypes_group(Inp, Dict = False):
    Temp = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = pd.DataFrame(index =Temp['Data Type'].unique(), columns = ['Features','Count'])
    for c in Temp['Data Type'].unique():
        Out.loc[Out.index == c, 'Features'] = [Temp.loc[Temp['Data Type'] == c].index.tolist()]
        Out.loc[Out.index == c, 'Count'] = len(Temp.loc[Temp['Data Type'] == c].index.tolist())
    Out.index.name = 'Data Type'
    Out = Out.reset_index(drop = False)
    Out['Data Type'] = Out['Data Type'].astype(str)
    
    if Dict:
        Out = dict(zip(Out['Data Type'], Out['Features']))
    return Out

def List_Print(Text, List, C = 'Blue', T = 'White'):
    BACK = {'Black': Back.BLACK, 'Red':Back.RED, 'Green':Back.GREEN, 'Yellow': Back.YELLOW, 'Blue': Back.BLUE,
            'Magenta':Back.MAGENTA, 'Cyan': Back.CYAN}
    FORE = {'Black': Fore.BLACK, 'Red':Fore.RED, 'Green':Fore.GREEN, 'Yellow':Fore.YELLOW, 'Blue':Fore.BLUE,
            'Magenta':Fore.MAGENTA, 'Cyan':Fore.CYAN, 'White': Fore.WHITE}
    print(BACK[C] + FORE[T] + Style.NORMAL + '%s:' % Text + Style.RESET_ALL + ' %s' % ', '.join(List))

data_info = Data_Plot(Data, Title = 'Telco Customer Churn')
dType = dtypes_group(Data, Dict = True)

Preprocessing

Integer Columns

List_Print(Text = 'Integer Columns', List = dType['int64'])
#
Data[dType['int64']] = Data[dType['int64']].astype('int32')
display(Data[dType['int64']].head(5).style.hide_index().set_precision(2))

Integer Columns: Senior Citizen, Tenure

Senior Citizen	Tenure
0	1
0	34
0	2
0	45
0	2

Float Columns

dType['float64'].append('Total Charges')
dType['object'].remove('Total Charges')
List_Print(Text = 'Float Columns', List = dType['float64'], C = 'Red')
#
def myfun(x):
    return re.sub(r"\s+$", "", x, flags=re.UNICODE)

Data['Total Charges'] = pd.to_numeric(Data['Total Charges'].map(lambda x: myfun(x)))
del myfun

# Imputing Missing Values
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
Temp = imp.fit_transform(Data['Total Charges'].values.reshape(-1,1))
Data['Total Charges'] = Temp.ravel()

Data[dType['float64']] = Data[dType['float64']].astype(float)
display(Data[dType['float64']].head(5).style.hide_index().set_precision(2))

Float Columns: Monthly Charges, Total Charges

Monthly Charges	Total Charges
29.85	29.85
56.95	1889.50
53.85	108.15
42.30	1840.75
70.70	151.65

Object Columns

List_Print(Text = 'Object Columns', List = dType['object'], C = 'Green')

Object Columns: customer ID, Payment Method, Paperless Billing, Contract, Streaming Movies, Streaming TV, Tech Support, Device Protection, Online Backup, Online Security, Internet Service, Multiple Lines, Phone Service, Dependents, Partner, Gender, Churn

Yes/No Columns

We can convert all Yes/No columns using as follows

\begin{cases} 0 &\mbox{No}\\ 1 &\mbox{Yes}\end{cases}

Temp = []
for c in dType['object']:
    if set(Data[c].unique().tolist()) == {'No', 'Yes'}:
        Temp.append(c)
List_Print(Text = 'Yes/No Columns', List = Temp, C = 'Yellow', T = 'Black')
Data[Temp] = Data[Temp].replace({'Yes':1, 'No':0}).astype('int32')
display(Data[Temp].head(5).style.hide_index().set_precision(2))
del Temp

Yes/No Columns: Paperless Billing, Phone Service, Dependents, Partner, Churn

Paperless Billing	Phone Service	Dependents	Partner	Churn
1	0	0	1	0
0	1	0	0	0
1	1	0	0	1
0	0	0	0	0
1	1	0	0	1

Internet Services

Some other columns can be converted similarly; however, we need to create a new feature.

Temp = []
for c in dType['object']:
    if set(Data[c].unique().tolist()) == {'No', 'No internet service', 'Yes'}:
        Temp.append(c)
List_Print(Text = 'Columns', List = Temp, C = 'Black', T = 'Cyan')

Columns: Streaming Movies, Streaming TV, Tech Support, Device Protection, Online Backup, Online Security

These Columns can be coded as follows

$$\mbox{InternetServiceType} = \begin{cases} 0 &\mbox{No internet service} \\ 1 &\mbox{No}\\ 2 &\mbox{Yes}\end{cases}$$

def myfun(x):
    if x == 'No internet service':
        return 0
    elif x == 'No':
        return 1
    else:
        return 2

Data[Temp] = Data[Temp].applymap(lambda x: myfun(x)).astype('int32')
display(Data[Temp].head(5).style.hide_index().set_precision(2))
del myfun, Temp

Streaming Movies	Streaming TV	Tech Support	Device Protection	Online Backup	Online Security
1	1	1	1	2	1
1	1	1	2	1	2
1	1	1	1	2	2
1	1	2	2	1	2
1	1	1	1	1	1

Moreover, note that,

List_Print(Text = 'Internet Service', List = Data['Internet Service'].unique(), C = 'Black', T = 'White')

Internet Service: DSL, Fiber optic, No

This Column can be coded as follows

$$\mbox{InternetServiceType} = \begin{cases} 0 &\mbox{No} \\ 1 &\mbox{DSL}\\ 2 &\mbox{Fiber optic}\end{cases}$$

def myfun(x):
    if x == 'No':
        return 0
    elif x == 'DSL':
        return 1
    else:
        return 2

Data['Internet Service'] = Data['Internet Service'].apply(lambda x: myfun(x)).astype('int32')
del myfun

Phone Services

Since, there is already a feature as Phone Service, for Multiple Lines, we can try $$ \mbox{MultipleLines} = \begin{cases} 0 &\mbox{No, No phone service}\\ 1 &\mbox{Yes}\end{cases} $$

Data['Multiple Lines'] = Data['Multiple Lines'].map(lambda x: 1 if x =='Yes' else 0).astype('int32')

Remaining Columns

dType = dtypes_group(Data, Dict = True)
List_Print(Text = 'Remaining Columns', List = dType[ 'object'], C = 'Red', T = 'White')

Remaining Columns: Contract, Gender, Payment Method, customer ID

Contract

List_Print(Text = 'Contract', List = Data['Contract'].unique(), C = 'Black', T = 'White')

Contract: Month-to-month, One year, Two year

\begin{cases} 0 &\mbox{Month-to-month}\\ 1 &\mbox{One year}\\ 2 &\mbox{Two year} \end{cases}

Data['Contract'] = Data['Contract'].replace({'Month-to-month':0, 'One year':1, 'Two year':2}).astype('int32')

Gender

List_Print(Text = 'Gender', List = Data['Gender'].unique(), C = 'Black', T = 'White')

Gender: Female, Male

$$ \mbox{Gender} = \begin{cases} 0 &\mbox{Female}\\ 1 &\mbox{Male}\end{cases} $$

Data['Gender'] = Data['Gender'].map(lambda x: 1 if x =='Male' else 0).astype('int32')

Payment Method

List_Print(Text = 'Payment Method', List = Data['Payment Method'].unique(), C = 'Black', T = 'White')

Payment Method: Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic)

In this case, we can not rank these values. Therefore,

Data = Data.join(pd.get_dummies(Data['Payment Method']).astype('int32'))
Data = Data.drop(columns = ['Payment Method'])
Data_types = dtypes_group(Data)
display(Data_types)

	Data Type	Features	Count
0	int32	[Device Protection, Credit card (automatic), B...	21
1	float64	[Monthly Charges, Total Charges]	2
2	object	[customer ID]	1

_ = Data_Plot(Data, Title = 'Telco Customer Churn')

Features with high variance

Moreover, high variance for some features can hurt our modeling process. For this reason, we would like to standardize features by removing the mean and scaling to unit variance.

def Feature_Normalize(X, PD):
    def List_Break(mylist, n = PD['word_break']):
        Out = []
        for x in mylist:
            y = x.split()
            if len(y)> n:
                z = ' '.join(y[:n])
                sep = np.arange(0, len(y), n)[1:]
                for n in sep:
                    z = z + '\n'+ ' '.join(y[n:])
            else:
                z = ' '.join(y)
            Out.append(z)
        return Out

    scaler = preprocessing.StandardScaler()
    X_std = scaler.fit_transform(X)
    X_std = pd.DataFrame(data = X_std, columns = X.columns)

    fig, ax = plt.subplots(2, 1, figsize = PD['figsize'])
    ax = ax.ravel()
    CP = [sns.color_palette("OrRd", 20), sns.color_palette("Greens", X.shape[1])]
    Names = ['Variance of the Features', 'Variance of the Features (Standardized)']
    Sets = [X, X_std]
    kws = dict(label='Feature\nVariance', aspect=10, shrink= .3)
    for i in range(len(ax)):
        Temp = Sets[i].var().sort_values(ascending = False).to_frame(name= 'Variance').round(2).T
        _ = sns.heatmap(Temp, ax=ax[i], annot=True, square=True,  cmap = CP[i],
                        linewidths = 0.8, vmin=0, vmax=Temp.max(axis =1)[0],  annot_kws={"size": PD['annot_text_size']},
                        cbar_kws=kws)
        if not PD['word_break'] == None:
            mylist = List_Break(Temp.T.index.tolist())
            _ = ax[i].xaxis.set_ticklabels(mylist)
        _ = ax[i].set_yticklabels('')
        _ = ax[i].set_title(Names[i], weight='bold', fontsize = 14)
        _ = ax[i].set_aspect(1)
        del Temp
    plt.subplots_adjust(hspace=PD['hspace'])
    Out = pd.DataFrame(data = X_std, columns = X.columns.tolist())
    return Out

X = Data.drop(columns = [Target, 'customer ID'])

PD = dict(figsize = (18, 7), hspace = 0.2, annot_text_size = 8, word_break = 2)
X = Feature_Normalize( Data.drop(columns = [Target, 'customer ID']), PD)

# Modifying dataset 
Data[X.columns.tolist()] = X

Feature Correlation

def Feature_Corr(df, PD, Target = Target):
    def List_Break(mylist, n = PD['word_break']):
        Out = []
        for x in mylist:
            y = x.split()
            if len(y)> n:
                z = ' '.join(y[:n])
                sep = np.arange(0, len(y), n)[1:]
                for n in sep:
                    z = z + '\n'+ ' '.join(y[n:])
            else:
                z = ' '.join(y)
            Out.append(z)
        return Out
    fig, ax = plt.subplots(1, 1, figsize = PD['figsize'])
    CM = df.corr().round(2)
    CM = CM.loc[(CM.index == Target)].drop(columns = Target).T.sort_values(by = Target).T
    kws = dict(label='Feature\nVariance', aspect=10, shrink= .3)
    _ = sns.heatmap(CM, ax=ax, annot=True, square=True,  cmap = PD['Colormap'], linewidths = 0.8, vmin=0,
                    vmax=CM.max(axis =1)[0],  annot_kws={"size": PD['annot_text_size']}, cbar_kws=kws)
    if not PD['word_break'] == None:
        mylist = List_Break(CM.T.index.tolist())
        _ = ax.xaxis.set_ticklabels(mylist)
    _ = ax.set_yticklabels('')
    _ = ax.set_aspect(1)

PD.update(Colormap = 'Greens')
Feature_Corr(Data, PD)

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Saving to a CSV

Data.to_csv (Path.split(".")[0]+'_STD.csv', index = None, header=True)

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References

1. Kaggle Dataset: Telco Customer Churn

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